Common pre-diagnostic features in individuals with different rare diseases represent a key for diagnostic support with computerized pattern recognition?

Abstract

BackgroundRare diseases (RD) result in a wide variety of clinical presentations, and this creates a significant diagnostic challenge for health care professionals. We hypothesized that there exist a set of consistent and shared phenomena among all individuals affected by (different) RD during the time before diagnosis is established.ObjectiveWe aimed to identify commonalities between different RD and developed a machine learning diagnostic support tool for RD.Methods20 interviews with affected individuals with different RD, focusing on the time period before their diagnosis, were performed and qualitatively analyzed. Out of these pre-diagnostic experiences, we distilled key phenomena and created a questionnaire which was then distributed among individuals with the established diagnosis of i.) RD, ii.) other common non-rare diseases (NRO) iii.) common chronic diseases (CD), iv.), or psychosomatic/somatoform disorders (PSY). Finally, four combined single machine learning methods and a fusion algorithm were used to distinguish the different answer patterns of the questionnaires.ResultsThe questionnaire contained 53 questions. A total sum of 1763 questionnaires (758 RD, 149 CD, 48 PSY, 200 NRO, 34 healthy individuals and 574 not evaluable questionnaires) were collected. Based on 3 independent data sets the 10-fold stratified cross-validation method for the answer-pattern recognition resulted in sensitivity values of 88.9% to detect the answer pattern of a RD, 86.6% for NRO, 87.7% for CD and 84.2% for PSY.ConclusionDespite the great diversity in presentation and pathogenesis of each RD, patients with RD share surprisingly similar pre-diagnosis experiences. Our questionnaire and data-mining based approach successfully detected unique patterns in groups of individuals affected by a broad range of different rare diseases. Therefore, these results indicate distinct patterns that may be used for diagnostic support in RD.

Development of a Social Network for People Without a Diagnosis (RarePairs): Evaluation Study

“…In the future, this questionnaire-based tool might be improved using additional information from, for example, wearables. Additionally, the previous study [ 16 ] used a combination of different AI methods (support vector machine, random forest, logistic regression, and linear discriminant analysis), with better results in clustering diseases [ 16 ]. Perhaps the use of those additional AI methods could also improve the matching algorithm of RarePairs.…”

“…For the most important part of the prototype, the matching algorithm, we resorted to a questionnaire named Q53 which was built during previous research in the working group [ 16 ]. Briefly, this questionnaire was built using patients’ experience.…”

“…In the previous study [ 16 ] which described the development of the questionnaire, the set of questions was then used by a matching algorithm combining the results of support vector machine, random forest, logistic regression, and linear discriminant analysis, to effectively classify a data set of approximately 1000 questionnaires of individuals with different RDs and other disease conditions into 4 different diagnostic categories [ 16 ]. Briefly, these diagnostic categories differentiated between RDs, chronic diseases (CDs), psychosomatic disorders, and other disease conditions.…”

“…In the second step, an existing data set (from previous study [ 16 ]) of answered Q53 questionnaires (n=973) from individuals with non-RDs and different RDs, such as neuromuscular diseases (eg, Pompe disease, amyotrophic lateral sclerosis), autoimmune diseases (eg, sarcoidosis, systemic lupus erythematosus), or rare metabolic diseases (eg, glycogen storage diseases), was used for prototypic evaluation of RarePairs.…”

Digital Triage Tool Using Artificial Intelligence and Patient History for Detecting Selected Neurological Diseases and Sensing the Bottleneck between Symptoms, Diagnosis, and Therapy

“…In a series of publications [1][2][3][4][5][6][7][8][9][10][11][12][13], the authors presented the initial results and the basic principle of the procedures that they have developed over the last 10 years,. This publication describes the implementation and results of the continuing task of using the AI system in physicians' offices and at university centers for rare diseases as a diagnosis-supporting tool and testing it with the boundary conditions of medical institutions in the field.…”